Multidimensional structure-function relationships in
human
β-cardiac myosin from population-scale
genetic variation
Julian R. Homburgera, Eric M. Greenb, Colleen Caleshuc, Margaret S. Sunithad, Rebecca E. Taylore, Kathleen M. Ruppele,f, Raghu Prasad Rao Metpallyg, Steven D. Colanh, Michelle Michelsi, Sharlene M. Dayj, Iacopo Olivottok,
Carlos D. Bustamantea,l, Frederick E. Deweym, Carolyn Y. Hon, James A. Spudichd,e,1,2, and Euan A. Ashleya,c,1,2
aDepartment of Genetics, Stanford University School of Medicine, Stanford, CA 94305;bMyoKardia, Inc., South San Francisco, CA 94080;cStanford Center for Inherited Cardiovascular Disease, Stanford University, Stanford, CA 94305;dInstitute for Stem Cell Biology and Regenerative Medicine, Bangalore 560065, India;eDepartment of Biochemistry, Stanford University School of Medicine, Stanford, CA 94305;fDepartment of Pediatrics (Cardiology), Stanford University School of Medicine, Stanford, CA 94305;gCollaborative Bioinformatics Lab, Geisinger Clinic, Danville, PA 17822;hDepartment of Cardiology, Boston Children’s Hospital, Boston, MA 02115;iDepartment of Cardiology, Erasmus Medical Center, 3015 CE Rotterdam, The Netherlands;jCardiovascular Division, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109;kReferral Center for Cardiomyopathies, Careggi University Hospital, Florence, Italy 50134;lDepartment of Biomedical Data Sciences, Stanford University, Stanford, CA 94305;mRegeneron Genetics Center, Tarrytown, NY 10591; andnCardiovascular Division, Brigham and Women’s Hospital, Boston, MA 02115
Contributed by James A. Spudich, May 5, 2016 (sent for review March 31, 2016; reviewed by Elizabeth McNally and David D. Thomas)
Myosin motors are the fundamental force-generating elements of muscle contraction. Variation in the humanβ-cardiac myosin heavy chain gene (MYH7) can lead to hypertrophic cardiomyopathy (HCM), a heritable disease characterized by cardiac hypertrophy, heart failure, and sudden cardiac death. How specific myosin variants alter motor function or clinical expression of disease remains incompletely under-stood. Here, we combine structural models of myosin from multiple stages of its chemomechanical cycle, exome sequencing data from two population cohorts of 60,706 and 42,930 individuals, and genetic and phenotypic data from 2,913 patients with HCM to identify regions of disease enrichment withinβ-cardiac myosin. We first developed com-putational models of the humanβ-cardiac myosin protein before and after the myosin power stroke. Then, using a spatial scan statistic modified to analyze genetic variation in protein 3D space, we found significant enrichment of disease-associated variants in the converter, a kinetic domain that transduces force from the catalytic domain to the lever arm to accomplish the power stroke. Focusing our analysis on surface-exposed residues, we identified a larger region significantly enriched for disease-associated variants that contains both the con-verter domain and residues on a single flat surface on the myosin head described as the myosin mesa. Notably, patients with HCM with variants in the enriched regions have earlier disease onset than patients who have HCM with variants elsewhere. Our study provides a model for integrating protein structure, larscale ge-netic sequencing, and detailed phenotypic data to reveal insight into time-shifted protein structures and genetic disease.
hypertrophic cardiomyopathy
|
myosin|
rare disease genetics|
genetic burdenM
yosin motors are molecular machines responsible for con-verting chemical energy into the mechanical force necessary for cell division, directed cell migration, vesicle trafficking, and muscle contraction (1). Variants in the humanβ-cardiac myosin heavy chain gene (MYH7), cause hypertrophic cardiomyopathy (HCM), a genetic disease of the heart muscle characterized by an asymmetrical thickening of the ventricular walls and a decrease in the ventricular chamber size. HCM is the most common heritable heart disease, with a prevalence of one in 500 individuals (2). Clinically, the course of HCM is variable, with some patients experiencing minimal symp-toms and others developing arrhythmia, heart failure, or sudden death (3). Relationships between genotype and disease expression in HCM have been challenging to establish due to the absence of large-scale genetic population data and lack of multicenter sharing of patient genetic and clinical data (4, 5).The MYH7 gene is highly constrained for genetic variation (6). Few loss-of-function variants are observed in population cohorts,
and identified pathogenic variants are mainly missense. However, although many missense genetic variants in MYH7 cause HCM, not all genetic changes within the gene lead to disease. There are competing hypotheses regarding the localization of pathogenic missense variants within MYH7, analysis of which can offer insight into the underlying mechanism of HCM. Investigators have sug-gested enrichment of pathogenic variants in many of the functional domains of β-cardiac myosin, including the converter domain, actin-binding site, and ATP-binding domain (7–9). However, others have suggested there is no regional enrichment for HCM variation within MYH7 (4, 10). These inconsistencies could be due to limited sample sizes or to a lack of reference cohorts for comparison. Without information about the natural distribution
Significance
Genetic variants in humanβ-cardiac myosin, which causes muscle contraction in the heart, can lead to hypertrophic cardiomyopathy (HCM), an inherited heart disease that can cause sudden death. New technologies have generated sequence data for large num-bers of patients with HCM and unaffected individuals. In this study, we compare the protein structural locations of genetic variants of patients with HCM and the general population to identify spatial regions of the myosin that have a higher than expected proportion of genetic variants associated with HCM and earlier age at di-agnosis. In addition, we develop new methods to interrogate the localization of genetic changes in protein structures. Our study demonstrates the power of combining clinical, genetic, and struc-tural data to gain insight into Mendelian disease.
Author contributions: J.R.H., E.M.G., M.S.S., R.E.T., K.M.R., C.D.B., C.Y.H., J.A.S., and E.A.A. designed research; J.R.H., E.M.G., C.C., M.S.S., R.E.T., K.M.R., R.P.R.M., S.D.C., M.M., S.M.D., I.O., F.E.D., C.Y.H., J.A.S., and E.A.A. performed research; J.R.H., C.C., M.S.S., R.P.R.M., S.D.C., M.M., S.M.D., I.O., F.E.D., C.Y.H., J.A.S., and E.A.A. contributed new reagents/analytic tools; J.R.H., E.M.G., C.C., R.E.T., K.M.R., C.D.B., J.A.S., and E.A.A. analyzed data; and J.R.H., E.M.G., K.M.R., J.A.S., and E.A.A. wrote the paper.
Reviewers: E.M., Northwestern University; D.D.T., University of Minnesota.
Conflict of interest statement: J.A.S. is a founder of and owns shares in Cytokinetics, Inc. and MyoKardia, Inc., biotechnology companies that are developing therapeutics that target the sarcomere. E.M.G. is an employee and owns shares in MyoKardia, Inc. E.A.A. is a founder of Personalis, Inc. C.D.B. is on the Scientific Advisory Boards ofAncestry.com, Personalis, Liberty Biosecurity, and Etalon DX, and is also a founder and chair of the Scientific Advisory Board of IdentifyGenomics.
Freely available online through the PNAS open access option. 1J.A.S. and E.A.A. contributed equally to this work.
2To whom correspondence may be addressed. Email: jspudich@stanford.edu or euan@ stanford.edu.
This article contains supporting information online atwww.pnas.org/lookup/suppl/doi:10. 1073/pnas.1606950113/-/DCSupplemental.
of rare variants within MYH7, it is impossible to distinguish regions of disease-variant enrichment from regions of increased genetic tolerance (11). In addition, some putatively pathogenic variants are later found at higher than expected allele frequen-cies in large, ethnically diverse population reference cohorts (12). The study suggesting enriched domains lacked reference cohorts; when a reference cohort was compared against genetic variants from a small sample of patients with HCM, the study failed to detect any significant enrichment for disease-associated variants (10). In addition, a focus on the linear sequence of the gene or previously discovered functional domains could overlook novel functional regions or enrichments spanning multiple do-mains. These discrepancies point to the need to test for regional HCM variant enrichment within MYH7 using both a large patient population and a large reference cohort while also accounting for the 3D structure ofβ-cardiac myosin.
Recent advances in next-generation sequencing technology have enabled the assembly of large datasets of human genetic variation in both unselected and disease-affected populations. Comparative analysis of these cohorts enables within-gene inference of disease burden and constraint, a measure of population tolerance to variation that can reveal insight into critical functional residues and disease etiology. The Exome Aggregation Consortium (ExAC) (13) and the DiscovEHR (14) cohort are exome sequencing cohorts of 60,706 and 42,930 individuals, respectively, that provide detailed information regarding the rates and types of genetic variation seen within disease genes. In addition, the Sarcomeric Human Cardio-myopathy Registry (SHaRe) was established as an international consortium of HCM investigators and currently contains detailed longitudinal clinical data on 2,913 patients with HCM who have undergone genetic testing. As large clinical and population se-quencing projects such as these become more prevalent, novel methods for statistical analysis of variant burden and constraint will be essential for gaining insight into disease and identifying intragene regions enriched from disease-associated variation.
We hypothesized that assessing regional genetic tolerance in the context of time-shifted 3D structures would reveal novel in-sights into MYH7 and possible hot spots in the myosin structure of pathogenic variants within HCM. Here, we compare genetic data from the SHaRe, an international HCM registry (15), with variants identified in large-scale exome sequencing projects (13) to identify regions of enrichment for HCM-associated variation within pre-and poststroke structures ofβ-cardiac myosin. In addition, we de-velop a general statistical framework based on a modified version of a spatial scan statistic to search for regions of increased disease-associated variation in 3D protein structures and surfaces. Finally, we take advantage of the clinical and phenotypic data in the SHaRe to examine clinical differences between groups of patients based upon variant location withinβ-cardiac myosin. We demon-strate the power of combining clinical, genetic, and structural data to make inferences regarding disease etiology and 3D structural hotspots for HCM variants.
Results and Discussion
We first compared the linear distribution of missense variants in patients with HCM (SHaRe) with missense variants in a population reference cohort (ExAC). The ExAC cohort contains sequencing information from 60,706 individuals who were part of disease-spe-cific (non-HCM) and population genetic studies, and the SHaRe database contains 2,913 individuals with HCM sequenced for MYH7. Although the ExAC reference contains some pathogenic sarcomere variants and likely some individuals with HCM, it is not enriched for individuals with the disease. We found 192 unique missense variants (in 474 patients with MYH7 variants) in the HCM cohort and 421 unique missense variants in the ExAC database (Dataset S1). In both cases, observed missense variants were very rare and the ma-jority were observed only once (Fig. 1), consistent with previous reports of constraint within the MYH7 gene (6). Further, the vast majority of SHaRe patients with a rare variant in MYH7 carry only one such variant (457 of 474 patients), whereas 16 carry two rare missense variants and a single patient carries three different variants.
Both disease (SHaRe) and population/reference (ExAC) variants are nonuniformly distributed throughout the gene, and we find a significant difference in the linear distribution of rare variants between these cohorts [Kolmogorov–Smirnov (KS) test: P= 5.0 × 10−11; Fig. 1]. Disease-associated missense variants are concentrated in the catalytic globular domain and the coiled-coil S2, consistent with some previous results (4) but in contrast to other recent comparisons (10). Even within these domains, however, distributions of disease and population variants are not the same (KS test: P= 0.003). In addition, missense variants in MYH7 in both the SHaRe and ExAC cohorts are extremely rare; the majority in both cohorts are observed only once (Fig. 1 B and C). These results suggest that the likelihood of MYH7 variants causing disease is due, in part, to their location within the gene. Because molecular motors act in 3D space, we sought a method to investigate patterns of genetic tolerance in the folded structure of humanβ-cardiac myosin protein. We used multitemplate homology modeling of other myosin proteins in the pre- and poststroke states to build 3D models of humanβ-cardiac myosin containing the hu-man ventricular light chains (Fig. 2 and Materials and Methods). These models represent two distinct phases of the actin-activated myosin chemomechanical cycle. Four fundamental regions of the myosin motor domain are included: the actin-binding site (Fig. 2, green residues), the ATP-binding pocket (red), the converter domain (blue), and the light chain-binding region or lever arm. In the prestroke state, the converter aligns with a relatively flat surface of the myosin head described as the myosin mesa. Based on its size (>20 nm2), flat topology, and high degree of evolu-tionary conservation, this feature has been proposed as an in-teraction site for intra- or intermolecular binding (16). Following the force-producing lever arm stroke of a myosin head, the motor is in its poststroke state (Fig. 2A) and the mesa falls out of alignment with the converter domain.
To identify 3D structural regions of interest, we applied a modified version of the spatial scan statistic (17, 18) to the prestroke and poststroke models ofβ-cardiac myosin S1. This statistic searches for spherical regions with an increased proportion of genetic variants in disease compared with reference cohorts. We defined any rare missense variants observed in patients with HCM in the MYH7 head as disease-associated (n= 103) and any variants seen only in the ExAC cohort as reference (or population) variants (n= 110). Twenty-two variants observed in both the SHaRe and ExAC cohorts were classified as disease-associated. In the myosin pre-stroke model, we find a striking increase in the proportion of disease-associated missense variation in a 15-Å sphere centered Fig. 1. Differences in the position of missense variants between HCM and population reference cohorts in humanβ-cardiac myosin. (A) Missense variants identified in SHaRe HCM patients are shown in red, and missense variants iden-tified in ExAC individuals are shown in blue. The height of each point is offset for visibility. (B) Minor allele count of MYH7 missense variants observed in SHaRe HCM probands. (C) Minor allele count of MYH7 missense variants in the ExAC database. Fifteen missense variants with a frequency above 0.005 are not shown.
on residue 736 (P= 0.001) (Fig. 3A andDataset S2). This region, covering a subset of the converter domain, contains 17 missense variants observed in disease and no missense variants observed in reference data (Fig. 3C). Using the poststroke model ofβ-cardiac myosin, we again observed enrichment of disease-associated vari-ants in a portion of the converter domain (P< 0.001) centered on residue 733 (Fig. S1).
Enrichment of disease-associated variants in both the pre- and poststroke states persists when including only variants formally classified as pathogenic or likely pathogenic (26 disease-associated of 103 total variants in the SHaRe;Supporting Information). Burden tests can also be sensitive to differences in the population preva-lence of disease or differences in the population frequency of var-iants. To ensure that our results were robust to these potential biases, we performed the same analysis limited to individuals of European descent, the largest population in both the SHaRe and ExAC cohorts (2,519 of 2,913 patients in the SHaRe and 89 of 103 disease-associated variants;Supporting Information). We identified very similar enriched regions in both the pre- and poststroke models, indicating that our analysis is not being significantly af-fected by population stratification. However, large cohorts from other populations may help identify other regions of the myosin enriched for disease variation.
During systolic contraction of the heart, the converter domain serves the critical function of transducing force by swinging about 70° from its prestroke position (Fig. 3B, lever arm projecting outward). Variants in the converter domain have been shown to alter muscle power output and kinetics (19, 20) and have been associated with worse outcomes in HCM (21, 22). We find that the converter domain is the only spherical region significantly enriched for disease variation in contrast to previous reports,
which hypothesized that there were many regions of enrichment throughout the myosin head (7–9) or others that found no regions of significant enrichment (10). Although location in an enriched region is not a necessary condition for pathogenicity, novel variants in regions strongly enriched for disease variation should be viewed with increased suspicion. In regions where nearby variants do not have similar effects, there will not be an enrichment of disease-associated variation. Our data provide a complementary line of evidence that variants in part of theβ-cardiac myosin converter domain are poorly tolerated and individuals carrying variants in this region are prone to development of HCM.
To replicate these results, we sought an independent source of disease-associated and population genetic variation. We curated publications from medical centers not yet affiliated with the SHaRe registry (Dataset S3andSupporting Information). We com-pared this set of 231 unique missense variants with 430 unique missense variants found in 42,930 exomes from unselected individ-uals in the Geisinger Health System sequenced by the Regeneron Genetics Center (DiscovEHR cohort) (14) (Dataset S4). The converter domain regions identified in both the prestroke and poststroke states showed enrichment of disease-associated vari-ants in the replication dataset (prestroke: P= 0.0079, poststroke: P= 4.1 × 10−4).
Surface regions of theβ-cardiac myosin have been suggested to be important functional domains implicated in HCM (16). We extended our analysis to examine surface regions of β-cardiac myosin and search for regions enriched for HCM-associated var-iants. We first defined surface-exposed amino acids by their ac-cessibility to a spherical probe with a radius of 2.5 Å (the approximate size of an amino acid side chain) and approximated the surface dis-tance between any two residues (23) (Supporting Information). As expected, surface distances tended to be shorter between amino acids nearby in the primary sequence (Fig. S2B, diagonal). In addition, there were many regions where amino acids far apart in the primary Fig. 2. Structural models of the human β-cardiac post- and prestroke
obtained by integrating data from solved crystal structures of homologous models. (A) Side view of myosin S1, with the relatively flat mesa at the top, in the poststroke state with important functional domains labeled: the actin-binding site (green residues), the ATP-actin-binding pocket (red), the converter (blue), and the light chain-binding region or lever arm. The converter and its associated lever arm are behind the plane of the figure and below the level of the mesa. (B) Myosin S1 in the prestroke state. Small changes within the globular head region with ADP and Pi in the nucleotide pocket result in a large∼70° rotation of the converter and lever arm. The converter is moved forward and up compared with the poststroke structure, and the lever arm is projecting forward out of the plane of the image. The distance traversed by the C-terminal end of the lever arm is∼10 nm, the stroke size of the motor.
Fig. 3. Spatial scan statistic identifies a spherical region of the converter do-main with an increased proportion of HCM-associated variants. (A) Same side view of the prestroke S1 motor domain as shown in Fig. 1B, without the light chains attached to theα-helical light chain-binding region of the S1. The orange residues define a sphere of residues in the motor domain, which is the only region significantly enriched for HCM variants. The S1 residues are colored as follows: orange, region enriched for HCM variants; blue, missense variants seen only in the ExAC; red, missense HCM variants seen in the SHaRe; light gray, all other residues. (B) Enriched region in the prestroke model of myosin from a different perspective. The view is directly down onto the surface. Coloring is as in A. (C) Number of HCM-variants (SHaRe) and non–disease-associated (ExAC) variants identified in the spherical enriched region (Left) and in the sum of all other parts of the myosin (Right).
sequence were close together on the surface of theβ-cardiac myosin (e.g., amino acids near residues 110 and 684).
The surface ofβ-cardiac myosin contains 568 of the 765 residues in the S1 domain (74%). Of these residues, 71 are associated with HCM (72% of all HCM variants) and 79 are found in the ExAC population (71% of all reference variants), suggesting that variants in both cohorts are evenly distributed between the surface and core of the protein (χ2: P = 0.51). We then applied our spatial scan statistic to the surface ofβ-cardiac myosin. Using the myosin pre-stroke model, we identified a region of the surface covering 277 of the 568 surface amino acid residues (P= 0.002; Fig. 4 A and B), including the converter domain and the mesa, which is highly enriched for disease-associated variation. Strikingly, the region contains 52 of the 71 surface HCM-associated missense variants and only 27 of the 79 surface non–disease-associated missense variants (Fig. 4D), whereas the remainder of the myosin surface (Fig. 4C) covers 291 residues and contains only 19 disease-associated variants compared with 52 non–disease-associated variants (Fig. 4D). The identified converter/mesa region was also enriched in the replication dataset (P= 4.3 × 10−5).
Using the same procedure to search the surface regions of the myosin poststroke structure, we detected a smaller enriched re-gion of 122 amino acid residues again covering the converter but with a reduced portion of the myosin mesa (Fig. S3), a domain that has been proposed to be a binding site for another protein structural element (16). During the myosin power stroke, the converter moves away from the mesa (compare Figs. 2 A and B, 2B, and 4 andFig. S3), so the enriched converter/mesa region is no longer contiguous and available for intra- or intermolecular interactions in the poststroke state (Fig. 2B and Fig. 4 A and B). The enriched amino acid residues in the poststroke model move significantly more in 3D space between the pre- and poststroke models (Wilcoxon test: P < 2 × 10−16) than other amino acid residues on the surface of the protein. This result suggests that myosin conformational changes during the actin-activated che-momechanical cycle may be important not only for transducing
force but also for modulating the size and shape of this surface region and altering its availability for binding to other protein structural elements. The functional importance of the converter and the mesa region is further supported by the presence of the binding site for omecamtiv mecarbil, a recently described small-molecule modulator of cardiac myosin currently in clinical trials for the treatment of heart failure (24, 25).
Next, we tested the myosin S2 fragment for regions enriched for disease-associated variation. The spatial scan analysis revealed that the first half of the S2 fragment is enriched for disease variants (P= 0.003;Fig. S4). This proximal part of S2 has been shown to bind to the aminoterminal part of myosin-binding protein C (MyBP-C) (26), a sarcomere protein that is also fre-quently mutated in patients with HCM (27). The enrichment of disease-associated variants in this region suggests that binding between myosin S2 and MyBP-C (and potentially other partners) is important for development of HCM.
To investigate the contribution of the genetically constrained regions to disease further, we compared the clinical features of patients with variants in these regions with the clinical features of patients with variants elsewhere in MYH7. The clinical profile of HCM is highly variable, with some patients living a normal lifespan with minimal symptoms and others dying suddenly or requiring cardiac transplantation at a young age (28). Similarly, age at presentation of HCM varies widely among patients, and earlier onset is correlated with a more severe phenotype (29). We find that HCM patients with a variant inside the spherical enriched region are 11.2 y younger at diagnosis [24.9 (SE= 1.0) vs. 36.1 (SE= 1.2) y old; P = 5.6 × 10−5; Fig. 5 A and B] than patients harboring other variants in the myosin head. The pres-ence of a variant in the HCM-enriched surface region is asso-ciated with a 10.0-y younger age at diagnosis [31.5 (SE= 2.4) vs. 41.5 (SE= 2.6) y old; P = 1.6 × 10−4] than in those patients with other surface variants (Fig. 5 C and D). In addition, we find an increased hazard for clinical outcomes in the surface enriched re-gion after adjusting for differences in age at diagnosis (hazard ratio= 1.918, P= 0.023), although not in the spherical enriched region (Fig. S5andSupporting Information). These findings demonstrate that analysis of genetic constraint in protein space can reveal do-mains with both increased disease burden and pathogenicity.
Our study demonstrates the power of integrating detailed structural information with large clinical and genetic databases to identify regions associated with functional importance and disease severity in Mendelian diseases. We find that variants associated with HCM are enriched in the β-cardiac myosin converter domain, where they lead to more severe outcomes. We provide the first evidence, to our knowledge, that similar clustering and pathoge-nicity are present in a surface spanning the converter domain and the relatively flat surface of the myosin catalytic domain called the myosin mesa (16). Because amino acid residues forming the mesa come from disparate locations in the nucleotide sequence, discovery of this region depends on integration of protein structural in-formation. The pronounced shift of the converter/mesa surface during the power stroke raises the mechanistic hypothesis that these variants exert their deleterious effect selectively in the prestroke state, perhaps by disrupting dynamic binding interactions. In sum-mary, these findings highlight the importance of considering data from human genetics in the context of the dynamic 3D protein structure, and illustrate an approach to structure/function analysis in genetic diseases.
Materials and Methods
SHaRe Database. The SHaRe is a multicenter database that pools deidentified patient-level data from established institutional datasets at participating sites. At the time of analysis, the registry contained clinical and genetic testing data on 2,913 patients with HCM, 514 with variants in MYH7. This database contains individuals from nine inherited disease centers throughout the world and includes demographic data, medical history, echocardiogram data, genetic testing results, and many other data relating to cardiac health and clinical outcomes.
Fig. 4. Surface spatial scan analysis identifies a larger surface region enriched for HCM-associated missense variation. (A) Similar view of the prestroke model to the view in Fig. 2B, looking directly down onto the mesa. The residues are colored as follows: orange, surface region enriched for HCM variants; blue, missense variants seen only in the ExAC; red, missense variants seen in the SHaRe; light gray, residues considered to be on the surface; dark gray, residues not considered to be on the surface. The HCM-enriched surface region identi-fied covers the entire mesa plus the adjoining converter domain. (B) Same side view of the prestroke S1 motor domain as shown in Figs. 1 and 2A. (C) View of the prestroke model viewing the side opposite the mesa. This surface is not enriched for HCM variants. (D) Number of HCM variants (SHaRe) and non– disease-associated (ExAC) variants identified in the surface enriched region (Left) and in the sum of all other parts of the myosin surface (Right).
ExAC. The ExAC (13) released data from 60,706 exomes from multiple se-quenced cohorts that are not enriched for rare diseases such as HCM. We downloaded ExAC data for the canonical MYH7 transcript ENST00000355349 on August 27, 2015.
Variant Filtering and Inclusion Criteria. Variants in MYH7 from the SHaRe database were filtered for quality purposes. Only exonic missense variants in MYH7 were included in the analysis. We included all exonic missense variants seen in HCM patients in clinical genetic testing. For comparison, we downloaded data from the MYH7 gene from the ExAC on August 27, 2015.
For the spatial scan and surface scan analyses, variants were considered to be one of two categories: disease-associated and reference. Any rare missense variants identified in HCM patients in the SHaRe cohort were considered to be disease-associated, regardless of their presence in the ExAC population. Rare missense variants identified only in the ExAC population were considered to be non–disease-associated, or reference, variants. Any variants identified in both the SHaRe and ExAC cohorts were considered to be disease-associated. Within the MYH7 S1 head, we identified 103 disease-associated variants (seen in the SHaRe) and 110 reference (or population) variants seen only in the ExAC cohort. Twenty-two variants observed in both the SHaRe and ExAC cohorts were classified as disease-associated.
Enrichment testing was performed by searching for regions of the myosin protein that had a significantly higher proportion of disease-associated variants than non–disease-associated variants. The ExAC population likely contains some individuals with cardiomyopathy induced by changes in MYH7; therefore, we cannot assume variants identified in the ExAC are not pathogenic. For example, there are multiple individuals carrying a well-known pathogenic variant for HCM in the MYBPC3 gene, p.Arg502Trp, in the ExAC cohort. However, the incidence of cardiomyopathies in the ExAC population is not expected to be greater than the incidence in the general population. A large proportion of the rare variants identified in MYH7 in the ExAC cohort are likely not associated
with cardiomyopathy. In contrast, rare variants identified in SHaRe HCM pa-tients are likely enriched for disease-causing variation.
We calculated the maximum population-specific allele frequency for each variant in the ExAC as the maximum frequency of the variant in any of the ExAC populations (excluding Finnish). To compare the burden of rare variants within each cohort, we removed from both sets (SHaRe and ExAC) any allele with a population-specific allele frequency higher than 1:2,000. This filter removed 16 variants in MYH7 from our ExAC list (of 421 total variants), six of which were also reported in the SHaRe list (of 192 total variants). Within the MYH7 head, only four ExAC variants were found at a higher frequency than 1:2,000 (of 136 total variants), two of which were also seen in the SHaRe cohort (of 105 total variants). For validation purposes, we also performed a subset of analyses including variants classified as“pathogenic” or “likely pathogenic” according to the American College of Medical Genetics and Genomics guidelines (30), although excluding variants of unknown significance, likely benign, and benign variants (Supporting Information). In addition, we performed a validation analysis using variants found in individuals of European ancestry from the ExAC and indi-viduals with a reported race of white to ensure that global population structure was not confounding our analysis (Supporting Information).
We generated an independent validation dataset combining previously published analyses of HCM variants from other medical centers with 42,930 exomes from the DiscovEHR sequencing project (14). We included only missense variants and removed variants with an allele frequency greater than 1 in 2,000 in the DiscovEHR exomes (Supporting Information).
Development of Humanβ-Cardiac Myosin Protein Models. We developed human β-cardiac myosin S1 models based on the known motor domain structural data to represent the human form of the cardiac myosin. We retrieved the protein se-quence of humanβ-cardiac myosin and the human cardiac light chains from the UniProt database (31): myosin heavy chain motor domain (MYH7), P12883; my-osin essential light chain (MLC1), P08590; and mymy-osin regulatory light chain (MLC2), P10916. We used a multitemplate homology modeling approach to build the structural coordinates of MYH7 (residues 1–840), MLC1 (residues 1–195), MLC2 (residues 1–166), and S2 (residues 841–1,280). We obtained the 3D structural model of S1 in the pre- and poststroke states by integrating the known structural data from solved crystal structures (details are provided in Sup-porting Information). Homology modeling of the prestroke structure was performed with template structures of the smooth muscle myosin motor domain (32) [Protein Data Bank (PDB) ID code 1BR1; with MgADP.AlF4 bound at the active site, which is thought to mimic most closely the ADP.Pi-bound state, or prestroke state, of the myosin] and the scallop smooth muscle myosin light chain domain (33) (PDB ID code 3TS5). The templates used for the modeling of the poststroke structure were obtained from the humanβ-cardiac myosin motor domain (25, 34) (PDB ID code 4P7H; no nu-cleotide in the active site) and the rigor structure from the squid myosin motor domain (35) (PDB ID code 3I5G; no nucleotide in the active site). Modeling was done using the MODELER package (36). Visualizations were performed using PyMOL version 1.7.4 (www.pymol.org).
Statistical Methods. Comparisons between the ExAC and SHaRe variant loca-tions in MYH7 were performed using the KS test statistic. All statistical analyses were performed in R version 3.1.2 (37), and many graphs were prepared using ggplot2 (38).
Spatial Scan Statistic. For the spatial scan analysis, we compared the locations of unique HCM-associated variants with the locations of reference variants. The spatial scan statistic exhaustively searches 3D windows of a predefined set of sizes and shapes throughout the humanβ-cardiac myosin molecule for regions with an increased proportion of HCM-associated variants. It identifies the 3D region of the protein with the largest binomial likelihood ratio test of enrichment of HCM-associated variants (17, 18) (Supporting Information). The maximum binomial ratio test statistic for the entire set of windows in the model was calculated, and significance was assessed through permutation of variant labels using 1,000 permutations. For validation, we performed the analysis above, removing all missense variants classified as variants of unknown signifi-cance using only missense variants observed in the European individuals (Supporting Information).
Surface Analysis. In addition to the spherical windows defined above, we define windows based upon the exposed surface of the humanβ-cardiac myosin mole-cule. For this analysis, we estimate the surface distance between any two amino acids based upon the solvent-excluded surface calculated by the MSMS program (23) (Supporting Information). Based upon this set of pairwise calculated dis-tances, we define planar surface regions of theβ-cardiac myosin for analysis as all of the amino acids within a certain distance of any given“center” amino acid. Fig. 5. Comparison of clinical phenotypes between enriched regions and
other regions inβ-cardiac myosin. (A) Age of diagnosis of patients with HCM variants in the enriched spherical converter region (orange, n= 45) compared with patients with variants in other parts of the myosin head (blue, n= 201) (Wilcoxon test: P= 6.7 × 10−5). Box plots show the median surrounded by the interquartile range (IQR), with whiskers extending to 1.5-fold the interquartile range (IQR). (B) Kaplan–Meier curves of age at diagnosis compared between HCM variants in the enriched spherical converter region (orange) and patients with variants in other parts of the myosin head (blue). Shading indicates 95% confidence intervals for the survival curve. (C) Age of diagnosis of patients with HCM variants in the enriched surface region (orange, n= 145) compared with patients with variants in other parts of the myosin head surface (blue, n= 46) (Wilcoxon test: P= 1.6 × 10−4). Box plots show the median and IQR, with whiskers extending to 1.5-fold the IQR. (D) Kaplan–Meier curves of age at di-agnosis compared between HCM variants in the enriched surface region (or-ange) and patients with variants in other parts of the myosin head surface (blue). Shading indicates 95% confidence intervals for the survival curve.
We exclude nonsurface amino acids, based upon their depth. We once again perform the spatial scan statistic as described above to identify surface regions of increased genetic burden.
S2 Fragment Analysis. We performed the spherical spatial scan statistic as de-scribed above to test for enrichment in the S2 fragment of myosin, including the region between amino acids 838 and 1,112 (Supporting Information). Clinical Phenotype Analysis. For the analysis of age at diagnosis, only known probands were included (n= 260 myosin head, n = 201 myosin surface). Patients with multiple MYH7 variants were excluded from the age at diagnosis analysis, because the presence of multiple variants causes an earlier onset of HCM. We
compared the primary diagnosis ages using a Wilcoxon test. The overall com-posite outcome combined the arrhythmic and heart failure outcomes, as well as the outcomes of atrial fibrillation, stroke, and all-cause death (Supporting Information). Individuals were considered to enter the study at their diagnosis age and were censored at their last known age. We compared hazard ratios for each region using the Cox proportional hazards model, adjusting for gender.
ACKNOWLEDGMENTS. We thank the investigators and participants in the SHaRe for their support of this project. We also thank Jonathan Fox for guidance during the early stages of the SHaRe. J.R.H. is supported by a Stanford Graduate Fellowship. R.E.T. is supported by NIH Grant F32 HL123247. This work supported by NIH Grants U01HG007436 (to C.D.B.), 1U01HG007708 (to E.A.A.), and U01HG006382 (to R.P.R.M.), and a grant from SAP (to C.D.B.),
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